



a_  :int     = {
    "joule": 1.0,
    "kilojoule": 1_000,
    "megajoule": 1_000_000,
    "gigajoule": 1_000_000_000,
    "wattsecond": 1.0,
    "watthour": 3_600,
    "kilowatthour": 3_600_000,
    "newtonmeter": 1.0,
    "calorie_nutr": 41_86.8,
    "kilocalorie_nutr": 4_186_800.00,
    "electronvolt": 1.6_0_2_1_7_6_6_3_4e-1_9,
    "britishthermalunit_it": 10_55.0_55_85,
    "footpound": 1.35_58_18,
}
def 			lowercase_				(A					:					Optional[int]						,   A					:					Any						,   A					:					Tuple ):



       if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
              snake_case__				:      List[str]        =      (
                  F'''Incorrect \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
                  F'''Valid values are: {", ".join(__lowerCAmelCase )}'''
              )
              raise ValueError(__lowerCAmelCase )
       return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
 import doctest

 doctest.testmod()







"""simple docstring"""


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


a				=logging.get_logger(__name__)

a				={
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}






class   __UpperCAmelCase		(      __lowerCAmelCase      ):
				A__ :  str    				=			'''deta'''
				A__ :  List[str]    				=			{
				    '''hidden_size''': '''d_model''',
				    '''num_attention_heads''': '''encoder_attention_heads''',
				}

				def __init__( self      ,					_lowerCamelCase=None      ,					_lowerCamelCase=900      ,					_lowerCamelCase=2048      ,					_lowerCamelCase=6      ,					_lowerCamelCase=2048      ,					_lowerCamelCase=8      ,					_lowerCamelCase=6      ,					_lowerCamelCase=1024      ,					_lowerCamelCase=8      ,					_lowerCamelCase=0.0      ,					_lowerCamelCase=True      ,					_lowerCamelCase="relu"      ,					_lowerCamelCase=256      ,					_lowerCamelCase=0.1      ,					_lowerCamelCase=0.0      ,					_lowerCamelCase=0.0      ,					_lowerCamelCase=0.0_2      ,					_lowerCamelCase=1.0      ,					_lowerCamelCase=True      ,					_lowerCamelCase=False      ,					_lowerCamelCase="sine"      ,					_lowerCamelCase=5      ,					_lowerCamelCase=4      ,					_lowerCamelCase=4      ,					_lowerCamelCase=True      ,					_lowerCamelCase=300      ,					_lowerCamelCase=True      ,					_lowerCamelCase=True      ,					_lowerCamelCase=1      ,					_lowerCamelCase=5      ,					_lowerCamelCase=2      ,					_lowerCamelCase=1      ,					_lowerCamelCase=1      ,					_lowerCamelCase=5      ,					_lowerCamelCase=2      ,					_lowerCamelCase=0.1      ,					_lowerCamelCase=0.2_5      ,					**_lowerCamelCase      ,					):
										if backbone_config is None:
																logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." )
																lowerCamelCase__   =CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"] )
										else:
																if isinstance(_lowerCamelCase      ,					_lowerCamelCase ):
																						lowerCamelCase__   =backbone_config.pop("model_type" )
																						lowerCamelCase__   =CONFIG_MAPPING[backbone_model_type]
																						lowerCamelCase__   =config_class.from_dict(_lowerCamelCase )

										lowerCamelCase__   =backbone_config
										lowerCamelCase__   =num_queries
										lowerCamelCase__   =max_position_embeddings
										lowerCamelCase__   =d_model
										lowerCamelCase__   =encoder_ffn_dim
										lowerCamelCase__   =encoder_layers
										lowerCamelCase__   =encoder_attention_heads
										lowerCamelCase__   =decoder_ffn_dim
										lowerCamelCase__   =decoder_layers
										lowerCamelCase__   =decoder_attention_heads
										lowerCamelCase__   =dropout
										lowerCamelCase__   =attention_dropout
										lowerCamelCase__   =activation_dropout
										lowerCamelCase__   =activation_function
										lowerCamelCase__   =init_std
										lowerCamelCase__   =init_xavier_std
										lowerCamelCase__   =encoder_layerdrop
										lowerCamelCase__   =auxiliary_loss
										lowerCamelCase__   =position_embedding_type
										# deformable attributes
										lowerCamelCase__   =num_feature_levels
										lowerCamelCase__   =encoder_n_points
										lowerCamelCase__   =decoder_n_points
										lowerCamelCase__   =two_stage
										lowerCamelCase__   =two_stage_num_proposals
										lowerCamelCase__   =with_box_refine
										lowerCamelCase__   =assign_first_stage
										if two_stage is True and with_box_refine is False:
																raise ValueError("If two_stage is True, with_box_refine must be True." )
										# Hungarian matcher
										lowerCamelCase__   =class_cost
										lowerCamelCase__   =bbox_cost
										lowerCamelCase__   =giou_cost
										# Loss coefficients
										lowerCamelCase__   =mask_loss_coefficient
										lowerCamelCase__   =dice_loss_coefficient
										lowerCamelCase__   =bbox_loss_coefficient
										lowerCamelCase__   =giou_loss_coefficient
										lowerCamelCase__   =eos_coefficient
										lowerCamelCase__   =focal_alpha
										super().__init__(is_encoder_decoder=_lowerCamelCase      ,					**_lowerCamelCase )

				@property
				def 						_a      ( self ):
										return self.encoder_attention_heads

				@property
				def 						_a      ( self ):
										return self.d_model






				def 						_a      ( self ):
										lowerCamelCase__   =copy.deepcopy(self.__dict__ )
										lowerCamelCase__   =self.backbone_config.to_dict()
										lowerCamelCase__   =self.__class__.model_type
										return output


